docs: def choropleth(df, projection=None,
                 hue=None,
                 scheme=None, k=5, cmap='Set1', categorical=False, vmin=None, vmax=None,
                 legend=False, legend_kwargs=None, legend_labels=None,
                 extent=None,
                 figsize=(8, 6), ax=None,
                 **kwargs):
      """
      Area aggregation plot.
  
      Parameters
      ----------
      df : GeoDataFrame
          The data being plotted.
      projection : geoplot.crs object instance, optional
          A geographic projection. For more information refer to `the tutorial page on projections
          <https://nbviewer.jupyter.org/github/ResidentMario/geoplot/blob/master/notebooks/tutorials/Projections.ipynb>`_.
      hue : None, Series, GeoSeries, iterable, or str, optional
          Applies a colormap to the output points.
      categorical : boolean, optional
          Set to ``True`` if ``hue`` references a categorical variable, and ``False`` (the default) otherwise. Ignored
          if ``hue`` is left unspecified.
      scheme : None or {"quantiles"|"equal_interval"|"fisher_Jenks"}, optional
          Controls how the colormap bin edges are determined. Ignored if ``hue`` is left unspecified.
      k : int or None, optional
          Ignored if ``hue`` is left unspecified. Otherwise, if ``categorical`` is False, controls how many colors to
          use (5 is the default). If set to ``None``, a continuous colormap will be used.
      cmap : matplotlib color, optional
          The `matplotlib colormap <http://matplotlib.org/examples/color/colormaps_reference.html>`_ to be used.
          Ignored if ``hue`` is left unspecified.
      vmin : float, optional
          Values below this level will be colored the same threshold value. Defaults to the dataset minimum. Ignored
          if ``hue`` is left unspecified.
      vmax : float, optional
          Values above this level will be colored the same threshold value. Defaults to the dataset maximum. Ignored
          if ``hue`` is left unspecified.
      legend : boolean, optional
          Whether or not to include a legend. Ignored if neither a ``hue`` nor a ``scale`` is specified.
      legend_values : list, optional
          The values to use in the legend. Defaults to equal intervals. For more information see `the Gallery demo
          <https://residentmario.github.io/geoplot/examples/largest-cities-usa.html>`_.
      legend_labels : list, optional
          The names to use in the legend. Defaults to the variable values. For more information see `the Gallery demo
          <https://residentmario.github.io/geoplot/examples/largest-cities-usa.html>`_.
      legend_kwargs : dict, optional
          Keyword arguments to be passed to `the underlying legend <http://matplotlib.org/users/legend_guide.html>`_.
      extent : None or (minx, maxx, miny, maxy), optional
          Used to control plot x-axis and y-axis limits manually.
      figsize : tuple, optional
          An (x, y) tuple passed to ``matplotlib.figure`` which sets the size, in inches, of the resultant plot.
      ax : AxesSubplot or GeoAxesSubplot instance, optional
          A ``matplotlib.axes.AxesSubplot`` or ``cartopy.mpl.geoaxes.GeoAxesSubplot`` instance. Defaults to a new axis.
      kwargs: dict, optional
          Keyword arguments to be passed to the underlying ``matplotlib`` `Polygon patches
          <http://matplotlib.org/api/patches_api.html#matplotlib.patches.Polygon>`_.
  
      Returns
      -------
      ``AxesSubplot`` or ``GeoAxesSubplot``
          The plot axis
  
      Examples
      --------
  
      A choropleth takes observations that have been aggregated on some meaningful polygonal level (e.g. census tract,
      state, country, or continent) and displays the data to the reader using color. It is a well-known plot type,
      and likeliest the most general-purpose and well-known of the specifically spatial plot types. It is especially
      powerful when combined with meaningful or actionable aggregation areas; if no such aggregations exist,
      or the aggregations you have access to are mostly incidental, its value is more limited.
  
      The ``choropleth`` requires a series of enclosed areas consisting of ``shapely`` ``Polygon`` or ``MultiPolygon``
      entities, and a set of data about them that you would like to express in color. A basic choropleth requires
      geometry, a ``hue`` variable, and, optionally, a projection.
  
      .. code-block:: python
  
          import geoplot as gplt
          import geoplot.crs as gcrs
          gplt.choropleth(polydata, hue='latdep', projection=gcrs.PlateCarree())
  
      .. image:: ../figures/choropleth/choropleth-initial.png
  
      Change the colormap with the ``cmap`` parameter.
  
      .. code-block:: python
  
          gplt.choropleth(polydata, hue='latdep', projection=gcrs.PlateCarree(), cmap='Blues')
  
      .. image:: ../figures/choropleth/choropleth-cmap.png
  
      If your variable of interest is already `categorical
      <http://pandas.pydata.org/pandas-docs/stable/categorical.html>`_, you can specify ``categorical=True`` to
      use the labels in your dataset directly. To add a legend, specify ``legend``.
  
      .. code-block:: python
  
          gplt.choropleth(boroughs, projection=gcrs.AlbersEqualArea(), hue='BoroName',
                          categorical=True, legend=True)
  
      .. image:: ../figures/choropleth/choropleth-legend.png
  
      Keyword arguments can be passed to the legend using the ``legend_kwargs`` argument. These arguments will be
      passed to the underlying ``matplotlib.legend.Legend`` instance (`ref
      <http://matplotlib.org/api/legend_api.html#matplotlib.legend.Legend>`_). The ``loc`` and ``bbox_to_anchor``
      parameters are particularly useful for positioning the legend. Other additional arguments will be passed to the
      underlying ``matplotlib`` `scatter plot <http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.scatter>`_.
  
      .. code-block:: python
  
          gplt.choropleth(boroughs, projection=gcrs.AlbersEqualArea(), hue='BoroName',
                          categorical=True, legend=True, legend_kwargs={'loc': 'upper left'})
  
      .. image:: ../figures/choropleth/choropleth-legend-kwargs.png
  
      Additional arguments not in the method signature will be passed as keyword parameters to the underlying
      `matplotlib Polygon patches <http://matplotlib.org/api/patches_api.html#matplotlib.patches.Polygon>`_.
  
      .. code-block:: python
  
          gplt.choropleth(boroughs, projection=gcrs.AlbersEqualArea(), hue='BoroName', categorical=True,
                          linewidth=0)
  
      .. image:: ../figures/choropleth/choropleth-kwargs.png
  
      Choropleths default to splitting the data into five buckets with approximately equal numbers of observations in
      them. Change the number of buckets by specifying ``k``. Or, to use a continuous colormap, specify ``k=None``. In
      this case a colorbar legend will be used.
  
      .. code-block:: python
  
          gplt.choropleth(polydata, hue='latdep', cmap='Blues', k=None, legend=True,
                          projection=gcrs.PlateCarree())
  
      .. image:: ../figures/choropleth/choropleth-k-none.png
  
      The ``choropleth`` binning methodology is controlled using by `scheme`` parameter. The default is ``quantile``,
      which bins observations into classes of different sizes but the same numbers of observations. ``equal_interval``
      will creates bins that are the same size, but potentially containing different numbers of observations.
      The more complicated ``fisher_jenks`` scheme is an intermediate between the two.
  
      .. code-block:: python
  
          gplt.choropleth(census_tracts, hue='mock_data', projection=gcrs.AlbersEqualArea(),
                  legend=True, edgecolor='white', linewidth=0.5, legend_kwargs={'loc': 'upper left'},
                  scheme='equal_interval')
  
      .. image:: ../figures/choropleth/choropleth-scheme.png
      """
      # Initialize the figure.
      fig = _init_figure(ax, figsize)
  
      if projection:
          projection = projection.load(df, {
              'central_longitude': lambda df: np.mean(np.array([p.x for p in df.geometry.centroid])),
              'central_latitude': lambda df: np.mean(np.array([p.y for p in df.geometry.centroid]))
          })
  
          # Set up the axis.
          if not ax:
              ax = plt.subplot(111, projection=projection)
      else:
          if not ax:
              ax = plt.gca()
  
      # Clean up patches.
      _lay_out_axes(ax, projection)
  
      # Immediately return if input geometry is empty.
      if len(df.geometry) == 0:
          return ax
  
      # Set extent.
      extrema = _get_envelopes_min_maxes(df.geometry.envelope.exterior)
      _set_extent(ax, projection, extent, extrema)
  
      # Format the data to be displayed for input.
      hue = _validate_hue(df, hue)
      if hue is None:
          raise ValueError("No 'hue' specified.")
  
      # Generate the coloring information, if needed. Follows one of two schemes, categorical or continuous,
      # based on whether or not ``k`` is specified (``hue`` must be specified for either to work).
      if k is not None:
          # Categorical colormap code path.
  
          # Validate buckets.
          categorical, k, scheme = _validate_buckets(categorical, k, scheme)
  
          if hue is not None:
              cmap, categories, hue_values = _discrete_colorize(categorical, hue, scheme, k, cmap, vmin, vmax)
              colors = [cmap.to_rgba(v) for v in hue_values]
  
              # Add a legend, if appropriate.
              if legend:
                  _paint_hue_legend(ax, categories, cmap, legend_labels, legend_kwargs)
          else:
              colors = ['steelblue']*len(df)
      elif k is None and hue is not None:
          # Continuous colormap code path.
          hue_values = hue
          cmap = _continuous_colormap(hue_values, cmap, vmin, vmax)
          colors = [cmap.to_rgba(v) for v in hue_values]
  
          # Add a legend, if appropriate.
          if legend:
              _paint_colorbar_legend(ax, hue_values, cmap, legend_kwargs)
  
      # Draw the features.
      if projection:
          for color, geom in zip(colors, df.geometry):
              features = ShapelyFeature([geom], ccrs.PlateCarree())
              ax.add_feature(features, facecolor=color, **kwargs)
      else:
          for color, geom in zip(colors, df.geometry):
              try:  # Duck test for MultiPolygon.
                  for subgeom in geom:
                      feature = descartes.PolygonPatch(subgeom, facecolor=color, **kwargs)
                      ax.add_patch(feature)
              except (TypeError, AssertionError):  # Shapely Polygon.
                  feature = descartes.PolygonPatch(geom, facecolor=color, **kwargs)
                  ax.add_patch(feature)
  
      return ax